feat: Re-Act autonomous agent minimal implementation

langgraph_backend/agent_tools.py

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+import logging
+import os
+
+from dotenv import load_dotenv
+from langchain_core.messages.ai import AIMessage
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.prebuilt import create_react_agent
+from langgraph.types import Command, interrupt
+
+from tools_tools import calc
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+load_dotenv()
+
+checkpointer = MemorySaver()
+tools = [calc]
+
+# --- LangChain LLM setup (Gemini, correct usage) ---
+model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=os.getenv("GOOGLE_API_KEY"))
+agent = create_react_agent(
+    model=model,
+    tools=tools,
+    checkpointer=checkpointer,
+)
+
+# Usage example
+config = {"configurable": {"thread_id": "research_session_1"}}
+
+
+async def invoke_agent(message: str, thread_id: str):
+    config = {"configurable": {"thread_id": thread_id}}
+
+    async for event in agent.astream({"messages": [{"role": "user", "content": message}]}, config=config):
+        print(event)
+        if "agent" in event:
+            response = [
+                {"type": "ai_msg", "content": m.content, "total_tokens": m.usage_metadata["total_tokens"], "tool_calls": m.tool_calls}
+                for m in event["agent"]["messages"]
+            ]
+        elif "tools" in event:
+            response = [{"type": "tool_resp", "content": m.content} for m in event["tools"]["messages"]]
+        yield response

langgraph_backend/app_tools.py

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+import json
+import logging
+import os
+from datetime import datetime
+from typing import Annotated, Any, Dict, List, Literal, Optional, TypedDict
+
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+
+from agent_tools import invoke_agent
+
+load_dotenv()
+
+# Today's Date
+DATE = datetime.now().strftime("%d %b, %Y")
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+app = FastAPI()
+CORS_ALLOWED_ORIGINS = os.getenv("ALLOWED_ORIGINS", ",").split(",")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ALLOWED_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Session management (in-memory for now)
+sessions: Dict[str, Dict[str, Any]] = {}
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "ok"}
+
+
+@app.post("/chat")
+async def chat(request: Request):
+    data = await request.json()
+    message = data.get("message")
+    thread_id = data.get("thread_id")
+
+    async def event_generator():
+        async for event in invoke_agent(message, thread_id):
+            # Format the event as SSE (Server-Sent Events)
+            event_data = json.dumps(event)
+            yield f"data: {event_data}\n\n"
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/plain",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Content-Type": "text/event-stream",
+        },
+    )
+
+
+@app.post("/abort")
+async def abort(request: Request):
+    data = await request.json()
+    session_id = data.get("session_id")
+    if session_id in sessions:
+        scraper = sessions[session_id]["scraper"]
+        await scraper.close()
+        del sessions[session_id]
+    return {"status": "aborted"}
+
+
+if __name__ == "__main__":
+    logger.info("Starting KnowledgeNet server...")
+    import uvicorn
+
+    uvicorn.run(app, host="127.0.0.1", port=5000)

langgraph_backend/tools_tools.py

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+from langchain_core.tools import tool
+
+@tool
+def calc(a: int, b: int) -> int:
+    """
+    Takes in two integers and returns their integer sum.
+    """
+    return str(a + b)